Feasibility of the edmonton symptom assessment system (ESAS) score as patient-reported outcomes (PROs) assessment in chronic myeloid leukemia patients receiving tyrosine kinase inhibitor therapy: A pilot Study
Notice bibliographique
Résumé
Abstract Introduction A substantial proportion of chronic myeloid leukemia (CML) patients experience tyrosine kinase inhibitor (TKI)-related toxicities. Despite their prevalence, it remains challenging to capture patient-reported outcomes (PROs) during routine clinic visits and monitoring them longitudinally due to limited time, resources, and inconvenience to the patients. Since 2022, Princess Margaret Cancer Centre has implemented the EPIC electronic medical record (EMR) system, used by ~80% of hospitals in North America. Through EPIC, all cancer patients receive an automated survey 24 hours before their appointment, prompting them to complete the Edmonton Symptom Assessment System (ESAS). ESAS is a validated self-report tool that quantifies symptom burden on a 0-10 scale across multiple domains, including fatigue, anxiety, pain, drowsiness, nausea, lack of appetite, depression, shortness of breath, and overall well-being. While ESAS is widely used in oncology and palliative care, its application in CML patients receiving TKI therapy remains unexplored, particularly for identifying early signs of intolerance and guiding clinical decision-making. This study aimed to evaluate the feasibility of using EMR-integrated ESAS scores to monitor symptom burden in CML patients, particularly in the context of TKI discontinuation due to intolerance. Patients and Methods This quality assurance project was approved and conducted at Princess Margaret Cancer Centre. From August 2022 to July 2025, our EMR system has captured over 1672 results of ESAS assessments in 502 patients (average 3.33 surveys per patient; 69.7 assessments per month). We selected 60 CML patients who completed at least one ESAS assessment during TKI therapy and retrospectively reviewed their clinical data to evaluate the feasibility of ESAS score assessment in the 60 CML patients. Patients were categorized into two groups: those who discontinued TKI due to intolerance (n=18) and those who continued therapy (n=42). Data collected included age, sex, TKI type used, total ESAS score, and the score in each domain of ESAS score. ESAS scores were analyzed as continuous variables and dichotomized using a threshold of ≥30 to define higher symptom burden. Statistical comparisons were performed to evaluate potential associations between symptom burden and TKI discontinuation. Results Of the 60 patients included, 18 (30%) discontinued TKI therapy due to intolerance. Median age was similar between the 2 groups: 51 years (range 48–71) in the discontinuation group vs. 54 years (range 48–71) in those who continued (p = 0.38). Discontinuation was more frequent among males (13/27, 48%) compared to females (5/25, 20%; p=0.042). TKIs most frequently used before discontinuation were dasatinib (n=8 out of 27 pts, 30%), bosutinib (n=3/8, 37%), imatinib (n=3/5, 60%), nilotinib (n=2/6, 33%), and asciminib (n=2/27, 7%). Among patients who continued therapy, the majority were on second-generation TKIs (n=15) or asciminib (n=25). The median total ESAS score among patients who discontinued TKI was 18.5 (range 4–40), and 17 (range 10–34) in those who did not discontinue (p = 0.81). Among individual symptoms, fatigue and anxiety had the highest mean scores (3.6 and 2.9, respectively) among patients who discontinued TKI therapy due to intolerance. Patients with total ESAS scores had numerically higher rates of TKI discontinuation due to adverse events (8/23 [35%] vs. 10/37 [27%] for scores <30; p = 0.52). Follow-up ESAS assessments at 6–12 months after TKI switch were available for 9 patients. Of these, 6 showed improvement in scores (median reduction: 12 ± 3), 2 had no change, and 1 had worsening ESAS score. Conclusion In this pilot study, higher ESAS scores were associated with a greater likelihood of TKI discontinuation due to intolerance, although the difference was not statistically significant. These findings support the potential utility of ESAS as a PRO monitoring tool in CML, helping to identify patients at risk of intolerance and guide proactive supportive care interventions. Additionally, ESAS may be valuable for longitudinal follow-up to assess symptom evolution after treatment changes. Prospective studies with larger cohorts are warranted to validate these observations and determine whether symptom-guided strategies can improve TKI adherence and clinical outcomes in CML.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».